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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Cloud Filtering Using a Bi-Spectral Spatial Coherence Approach

The research in this paper focuses on describing a technique developed for cloud filtering using a bi-spectral approach on GOES-8/9 Imager data. The application was developed for use with infrared retrievals of geophysical parameters in mind, where cloud cover contaminates the derived product. However, numerous potential applications of the technique exist. The technique will be described and a preliminary validation of the algorithm will be presented. Although initially based on the spatial coherence approach from Coakley and Brethereton (1982), it has evolved to utilize a difference image of the I I and 3.9 micrometer channels on the GOES-8/9 Imager. This image is very similar to that produced by Nelson and Ellrod (1996). During the daytime the technique makes use of the varying solar reflectance in the 3.9 micrometer channel by clouds and land to identify cloudy pixels. While at night, the technique makes use of the varying emissivity of the clouds in the scene to discriminate between clear and cloudy pixels. The technique applies three basic threshold tests to produce the final cloud filtered image: 1) a standard deviation threshold to detect the spatial variance in the scene, 2) a difference threshold between adjacent pixels, and 3) a simple infrared temperature threshold. The first test is applied to the entire image at once, then in a second pass the next two tests are applied. The final infrared temperature threshold is only meant to identify the coldest clouds that might pass the previous tests. The technique performs well during the daytime, while nighttime performance is degraded but is promising. The technique has proven to be robust and shows great promise of meeting its original goal of cloud filtering for use in an infrared retrieval algorithm for use in climate studies.

Guillory, Anthony R.↗
NASA NTRS1991

A simplified wind vector algorithm for satellite scatterometers

A simplified algorithm is derived for retrieving wind vectors from microwave scatterometer observations. The azimuthal dependence of the sea-surface radar cross section is modeled by a double-cosine function, rather than the traditional second-order cosine expansion. The algorithm is tested using the aircraft scatterometer observations obtained during the AAFE-RADSCAT Experiment in 1975 and 1976. There is little difference between the performance of the simplified algorithm and the more involved least-squares searching algorithm. The AAFE-RADSCAT aircraft observations are sampled so as to simulate the three-look satellite scatterometer NSCAT that will be launched on the Japanese Advanced Earth Observing Satellite. The results of the retrievals indicate that it is probably not possible to uniquely determine the wind direction using just NSCAT observations because of a 180 degree ambiguity. However, if forecast models can predict the wind direction to an accuracy of +/- 90 deg, thereby eliminating the ambiguity, then the results indicate that NSCAT can determine the wind direction to an accuracy of about +/- 20 deg. Better performance is obtained when the three observations are the same polarization (either v-pol or h-pol), as compared to using a mix of v-pol and h-pol observations.

Wentz, Frank J.↗
DOE OSTI2024

Stochastic minibatch approach to the ptychographic iterative engine

The ptychographic iterative engine (PIE) is a widely used algorithm that enables phase retrieval at nanometer-scale resolution over a wide range of imaging experiment configurations. By analyzing diffraction intensities from multiple scanning locations where a probing wavefield interacts with a sample, the algorithm solves a difficult optimization problem with constraints derived from the experimental geometry as well as sample properties. The effectiveness at which this optimization problem is solved is highly dependent on the ordering in which we use the measured diffraction intensities in the algorithm, and random ordering is widely used due to the limited ability to escape from stagnation in poor-quality local solutions. In this study, we introduce an extension to the PIE algorithm that uses ideas popularized in recent machine learning training methods, in this case minibatch stochastic gradient descent. Our results demonstrate that these new techniques significantly improve the convergence properties of the PIE numerical optimization problem.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Aboveground Biomass Estimation Using NISAR Simulated ALOS-2 Time Series Data

Aboveground biomass (AGB) is a critical parameter to better understand the global carbon cycle and to develop sustainable forest management. However, a large uncertainty prevails. L-band SAR data have demonstrated strong potential to accurately retrieve AGB over low-biomass regions (<100 Mg ha-1). The upcoming NASA-ISRO Synthetic Aperture Radar mission will collect data at L- and S-band over earth’s landmass with a repeat period of 12 days, allowing us to have ample data for monitoring biomass and its dynamics. One of the key science requirements of the mission is to produce annual AGB maps at 1-ha resolution with RMS accuracy of 20 Mg/ha for 80 percentage of area over low-biomass regions in Calibration/Validation sites. The NISAR biomass algorithm will generate AGB maps based on the parameterization of semi-empirical model along with NISAR time-series dual pol data (HH and HV). To calibrate and validate the model for mission requirements, the mission will use reference estimates of AGB produced from ground inventory plots and airborne LiDAR data collected over selected sites distributed across different global ecoregions. This paper presents the initial results of the calibration/validation of the NISAR AGB retrieval algorithm over the Lenoir Landing (LENO), Alabama, USA site using NISAR simulated ALOS-2 time series data. Five multi-temporal dual-pol HH and HV NISAR Simulated ALOS 2 data collections were used as input to assess the performance of the model. The model AGB retrieval results shows that the NISAR model was able to achieve RMS accuracy within 20 Mg/ha.

Ramachandran, Naveen [Jet Propulsion Laboratory, C↗
DOE OSTI2021

Triple-frequency radar retrieval of microphysical properties of snow

Abstract. An algorithm based on triple-frequency (X, Ka, W) radar measurements that retrieves the size, water content and degree of riming of ice clouds is presented. This study exploits the potential of multi-frequency radar measurements to provide information on bulk snow density that should underpin better estimates of the snow characteristic size and content within the radar volume. The algorithm is based on Bayes' rule with riming parameterised by the “fill-in” model. The radar reflectivities are simulated with a range of scattering models corresponding to realistic snowflake shapes. The algorithm is tested on multi-frequency radar data collected during the ESA-funded Radar Snow Experiment For Future Precipitation Mission. During this campaign, in situ microphysical probes were mounted on the same aeroplane as the radars. This nearly perfectly co-located dataset of the remote and in situ measurements gives an opportunity to derive a combined multi-instrument estimate of snow microphysical properties that is used for a rigorous validation of the radar retrieval. Results suggest that the triple-frequency retrieval performs well in estimating ice water content (IWC) and mean mass-weighted diameters obtaining root-mean-square errors of 0.13 and 0.15, respectively, for log 10IWC and log 10Dm. The retrieval of the degree of riming is more challenging, and only the algorithm that uses Doppler information obtains results that are highly correlated with the in situ data.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2001

Retrieving LAI and FPAR from MERIS Data With Advanced Algorithms

The primary motivation of this research activity is to document the feasibility of deriving global fields of LAI (leaf area index) and FPAR (fraction of photosynthetically active radiation absorbed by vegetation) from atmosphere corrected ESA (European Space Agency) MERIS (Medium Resolution Imaging Spectrometer) data, validate and evaluate the MERIS LAI and FPAR products by intercomparison with MODIS (Moderate Resolution Imaging Spectrometer) LAI and FPAR products and with field data collected at the EOS Core sites distributed around the world. Our activities during Year 1 (January through December, 2001) have focused on the modification of the MODIS LAI/FPAR algorithm to facilitate its ability to process MERIS data. We considered a related but wider problem, i.e., fusion of biophysical parameters derived from data acquired by spectroradiometers of different spectral bands and different resolutions.

Myneni, Ranga B.↗
NASA NTRS2019

Advances in Neural Network Detection and Retrieval of Multilayer Clouds for CERES Using Multispectral Satellite Data

An artificial neural network (ANN) algorithm, employing several Aqua MODIS infrared channels, the retrieved total cloud visible optical depth, and vertical humidity profiles is trained to detect multilayer (ML) ice-over-water cloud systems as identified by matched CloudSat and CALIPSO (CC) data. The multilayer ANN, or MLANN, algorithm is also trained to retrieve the optical depth and the top and base heights of the upper-layer ice clouds in ML systems. The trained MLANN was applied to independent MODIS data resulting in a combined ML and single layer hit rate of 80% (77%) for nonpolar regions during the day (night). The results are more accurate than currently available methods and the previous version of the MLANN. Upper-layer cloud top and base heights are accurate to ±1.2 km and ±1.6 km, respectively, while the uncertainty in optical depth is ±0.457 and ±0.556 during day and night, respectively. Areas of further improvement and development are identified and will be addressed in future versions of the MLANN.

CERES↗
NASA NTRS1993

TOPEX/POSEIDON Microwave Radiometer (TMR): III. Wet Troposphere Range Correction Algorithm and Pre-Launch Error Budget

The sole mission function of the TOPEX/POSEIDON Microwave Radiometer (TMR) is to provide corrections for the altimeter range errors induced by the highly variable atmospheric water vapor content. The three TMR frequencies are shown to be near-optimum for measuring the vapor-induced path delay within an environment of variable cloud cover and variable sea surface flux background. After a review of the underlying physics relevant to the prediction of 5-40 GHz nadir-viewing microwave brightness temperatures, we describe the development of the statistical, iterative algorithm used for the TMR retrieval of path delay. Test simulations are presented which demonstrate the uniformity of algorithm performance over a range of cloud liquid and sea surface wind speed conditions...

Keihm, S. J.↗
NASA NTRS2010

A Framework for the Generation and Dissemination of Drop Size Distribution (DSD) Characteristics Using Multiple Platforms

Proper characterization of the precipitation drop size distribution (DSD) is integral to providing realistic and accurate space- and ground-based precipitation retrievals. Current technology allows for the development of DSD products from a variety of platforms, including disdrometers, vertical profilers and dual-polarization radars. Up to now, however, the dissemination or availability of such products has been limited to individual sites and/or field campaigns, in a variety of formats, often using inconsistent algorithms for computing the integral DSD parameters, such as the median- and mass-weighted drop diameter, total number concentration, liquid water content, rain rate, etc. We propose to develop a framework for the generation and dissemination of DSD characteristic products using a unified structure, capable of handling the myriad collection of disdrometers, profilers, and dual-polarization radar data currently available and to be collected during several upcoming GPM Ground Validation field campaigns. This DSD super-structure paradigm is an adaptation of the radar super-structure developed for NASA s Radar Software Library (RSL) and RSL_in_IDL. The goal is to provide the DSD products in a well-documented format, most likely NetCDF, along with tools to ingest and analyze the products. In so doing, we can develop a robust archive of DSD products from multiple sites and platforms, which should greatly benefit the development and validation of precipitation retrieval algorithms for GPM and other precipitation missions. An outline of this proposed framework will be provided as well as a discussion of the algorithms used to calculate the DSD parameters.

Wolf, David B.↗
NASA NTRS1994

On the Validation of Satellite-Derived Sea Ice Surface Temperature

The surface temperature of sea ice controls the rate of ice growth and heat exchange between the ocean and the atmosphere. An algorithm for the satellite retrieval of ice surface temperature has recently been published, but due to the lack of validation data has not been extensively tested. In this paper, data from a recent Arctic field experiment is used in an attempt to validate that algorithm. While the procedure is, in principle, straightforward, we demonstrate that validation is complicated by a variety of factors, including incorrectly assumed atmospheric conditions, undetected clouds in the satellite data, spatial and temporal variability in the surface temperature field, and surface and satellite measurement errors. Comparisons between surface temperatures determined from upwelling broadband longwave radiation, spatial measurements of narrow-band radiation, thermocouples buried just below the snow surface, and narrow-band satellite data show differences of 1 to 3 deg. C. The range in these independent measurements indicates the need for specially designed validation experiments utilizing narrow-band radiometers on aircraft to obtain broad spatial coverage.

Key, J.↗
NASA NTRS2014

Comparison of SMOS and SMAP Soil Moisture Retrieval Approaches Using Tower-based Radiometer Data over a Vineyard Field

The objective of this study was to compare several approaches to soil moisture (SM) retrieval using L-band microwave radiometry. The comparison was based on a brightness temperature (TB) data set acquired since 2010 by the L-band radiometer ELBARA-II over a vineyard field at the Valencia Anchor Station (VAS) site. ELBARA-II, provided by the European Space Agency (ESA) within the scientific program of the SMOS (Soil Moisture and Ocean Salinity) mission, measures multiangular TB data at horizontal and vertical polarization for a range of incidence angles (30-60). Based on a three year data set (2010-2012), several SM retrieval approaches developed for spaceborne missions including AMSR-E (Advanced Microwave Scanning Radiometer for EOS), SMAP (Soil Moisture Active Passive) and SMOS were compared. The approaches include: the Single Channel Algorithm (SCA) for horizontal (SCA-H) and vertical (SCA-V) polarizations, the Dual Channel Algorithm (DCA), the Land Parameter Retrieval Model (LPRM) and two simplified approaches based on statistical regressions (referred to as 'Mattar' and 'Saleh'). Time series of vegetation indices required for three of the algorithms (SCA-H, SCA-V and Mattar) were obtained from MODIS observations. The SM retrievals were evaluated against reference SM values estimated from a multiangular 2-Parameter inversion approach. The results obtained with the current base line algorithms developed for SMAP (SCA-H and -V) are in very good agreement with the reference SM data set derived from the multi-angular observations (R2 around 0.90, RMSE varying between 0.035 and 0.056 m3m3 for several retrieval configurations). This result showed that, provided the relationship between vegetation optical depth and a remotely-sensed vegetation index can be calibrated, the SCA algorithms can provide results very close to those obtained from multi-angular observations in this study area. The approaches based on statistical regressions provided similar results and the best accuracy was obtained with the Saleh methods based on either bi-angular or bipolarization observations (R2 around 0.93, RMSE around 0.035 m3m3). The LPRM and DCA algorithms were found to be slightly less successful in retrieving the 'reference' SM time series (R2 around 0.75, RMSE around 0.055 m3m3). However, the two above approaches have the great advantage of not requiring any model calibrations previous to the SM retrievals.

Soil Moisture↗
DOE OSTI2019

Holographic phase retrieval and reference design

Here, a general mathematical framework and recovery algorithm is introduced for the holographic phase retrieval problem. In this problem, which arises in holographic coherent diffraction imaging, a 'reference' portion of the signal to be recovered via phase retrieval is a priori known from experimental design. A generic formula is additionally derived for the expected recovery error when the measurement data is corrupted by Poisson shot noise. This facilitates an optimization perspective towards reference design and analysis. We employ this optimization perspective towards quantifying the performance of various reference choices.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Single Field-of-View Sounding Atmospheric Products

The Single Field-of-view Sounder Atmospheric Products (SiFSAP) answer the need for a novel high spatial resolution atmospheric data product for major hyper-spectral infrared (IR) sounder missions. SiFSAP include a complete set of atmospheric vertical profiles, cloud, and surface properties, which are physically retrieved from top-of-atmosphere (TOA) spectral radiances under all-sky conditions via a rigorously defined radiative transfer relationship. By using a state-of-art fast radiative transfer model and a carefully designed optimal estimation based physical retrieval scheme, the SiFSAP algorithm ensures both an ultra-fast data processing speed needed for operational weather applications and the radiometric consistency desired by long-term climate studies. SiFSAP supplement existing operational products by providing data at the native resolution of the sounder instruments, the direct and accurate retrieval of cloud scattering properties, and the establishment of ‘radiance closure.’

Wan Wu↗
NASA NTRS2017

Assimilation of MODIS Dark Target and Deep Blue Observations in the Dust Aerosol Component of NMMB-MONARCH version 1.0

A data assimilation capability has been built for the NMMB-MONARCH chemical weather prediction system, with a focus on mineral dust, a prominent type of aerosol. An ensemble-based Kalman filter technique (namely the local ensemble transform Kalman filter - LETKF) has been utilized to optimally combine model background and satellite retrievals. Our implementation of the ensemble is based on known uncertainties in the physical parametrizations of the dust emission scheme. Experiments showed that MODIS AOD retrievals using the Dark Target algorithm can help NMMB-MONARCH to better characterize atmospheric dust. This is particularly true for the analysis of the dust outflow in the Sahel region and over the African Atlantic coast. The assimilation of MODIS AOD retrievals based on the Deep Blue algorithm has a further positive impact in the analysis downwind from the strongest dust sources of the Sahara and in the Arabian Peninsula. An analysis-initialized forecast performs better (lower forecast error and higher correlation with observations) than a standard forecast, with the exception of underestimating dust in the long-range Atlantic transport and degradation of the temporal evolution of dust in some regions after day 1. Particularly relevant is the improved forecast over the Sahara throughout the forecast range thanks to the assimilation of Deep Blue retrievals over areas not easily covered by other observational datasets.The present study on mineral dust is a first step towards data assimilation with a complete aerosol prediction system that includes multiple aerosol species.

atmospheric dust↗
NASA NTRSDate not supplied

Single Field-of-View Sounding Atmospheric Products

The Single Field-of-view Sounder Atmospheric Products (SiFSAP) answer the need for a novel high spatial resolution atmospheric data product for major hyper-spectral infrared (IR) sounder missions. SiFSAP include a complete set of atmospheric vertical profiles, cloud, and surface properties, which are physically retrieved from top-of-atmosphere (TOA) spectral radiances under all-sky conditions via a rigorously defined radiative transfer relationship. By using a state-of-art fast radiative transfer model and a carefully designed optimal estimation based physical retrieval scheme, the SiFSAP algorithm ensures both an ultra-fast data processing speed needed for operational weather applications and the radiometric consistency desired by long-term climate studies. SiFSAP supplement existing operational products by providing data at the native spatial resolution of the sounder instruments, the direct and accurate retrieval of cloud scattering properties, and the establishment of ‘radiance closure.’

single-field-of-view, retrieval, hyperspectral sou↗
NASA NTRS1982

Contrast reduction by the atmosphere and retrieval of nonuniform surface reflectance

A radiative transfer model is developed which gives the upward radiance at nadir for any 1-D Lambertian surface reflectance. This model is used to depict the atmospheric effect on the transmittance of contrast for any 1-D surface reflectance. Here by contrast we mean a general variation of the radiation field across the image. With the aid of this model an inversion algorithm is developed for retrieval of true surface reflectance from high resolution satellite data (e.g., Landsat). This inversion technique can be a useful tool for extraction of surface reflectance from satellite data in the case of a surface reflectance variable in one dimension only (e.g., seashore or near borders of big fields). A sensitivity study of the inversion procedure on the knowledge of atmospheric parameters and sensor calibration was performed. It is shown that this inversion technique is stable even in the presence of errors in the sensor calibration and the atmospheric parameters. The method was applied to Landsat data in two wavelengths. The results show reasonable dependence of the derived surface reflectance on the distance from the seashore.

Mekler, Y.↗
NASA NTRS1992

Water vapor column abundance retrievals during FIFE

A variation of the modified Langley plot algorithm is reported here and applied to the retrieval of atmospheric water column abundance from a filtered sunphotometer. In this new methodology an absorption data base (LOWTRAN 7) is used to compute a water abundance versus transmittance curve of growth, rather than the square-root dependence previously assumed. Validation of the technique is provided from an uncertainty analysis, and plans to further validate using Fourier transform spectrometers are detailed. The new sunphotometer technique is used to report local column water vapor during the First ISLSCP Field Experiment (FIFE), and comparisons are made with abundances retrieved via FIFE radiosonde observations. The sunphotometer data can best be utilized, however, to in turn validate data from airborne or in-orbit measurements of water vapor. With these flight sensors, horizontal and topographic variability within the scene can be viewed. An example of the airborne data set is given using an image from the airborne visible infrared imaging spectrometer, as acquired on August 31, 1990.

Bruegge, Carol J.↗